Autonomous vehicles (AVs) are set to transform transportation by providing safer, more efficient, and accessible mobility solutions. However, deploying AV systems requires designers to ensure these vehicles can navigate complex, dynamic traffic environments safely and precisely. A vital component of this capability is the ability to predict the behavior of surrounding road users, yet achieving reliable predictions is a complex task. This thesis investigates several challenges in trajectory and intention prediction for autonomous driving, focusing on contextual awareness, probabilistic modeling, and differential motion constraints.

A primary focus of this thesis is context awareness, which includes interaction-aware (agent-to-agent) and environment-aware (road-to-agent) modeling. Early approaches to context awareness involved manually crafting interaction features. While effective, these methods rely on predefined heuristics and often scale poorly as environmental complexity increases. To address these limitations, the thesis adopts a graph-based approach that offers greater flexibility and expressiveness. By constructing relational graphs, graph neural networks can be used to learn agent interactions and environmental context in a data-driven manner. The thesis proposes several context-aware models and provides an extensive evaluation of their mechanisms, highlighting their overall impact on prediction performance.

Another core theme of this thesis is addressing the inherent uncertainty and non-determinism of traffic environments. This involves creating models that provide probabilistic predictions. Given the multimodal nature of road-traffic agent behavior, it is also important to design methods that offer multiple candidate predictions for a single condition, enabling AVs to account for different possible future outcomes. This thesis proposes several context-aware frameworks that leverage probabilistic modeling, illustrating how ensemble methods, mixture density networks, and diffusion-based generative models can be adapted to provide uncertainty estimates and multimodal predictions.

While neural networks are well-suited for capturing the complex dynamics of traffic, they do not inherently ensure that outputs conform to physical laws, which poses risks in safety-critical applications. To address this, the thesis incorporates differential motion constraints into the prediction framework to ensure that predicted trajectories are accurate, physically feasible, and robust to noise. In addition to improving prediction accuracy, it is demonstrated how this integration enhances interpretation, extrapolation, and generalization capabilities.

The use of neural ordinary differential equations is a central component of this thesis, providing a data-driven approach to modeling the motion of agents—such as pedestrians—that are challenging to describe using physical laws or rule-based methods. The thesis investigates their application beyond motion prediction to a wide array of sequence modeling tasks, analyzing the effects of numerical integration techniques, stability regions, and initialization methods on model performance. A key contribution is the proposal of a stability-informed initialization (SII) technique, which significantly enhances model convergence, training stability, and prediction accuracy across various learning benchmarks.